Cogent Economics & Finance

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Cogent Economics & Finance
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/oaef20
Stock market reaction to inflation announcement
in the Indian stock market: A sectoral analysis
Gurmeet Singh & Lakshmi Padmakumari |
To cite this article: Gurmeet Singh & Lakshmi Padmakumari | (2020) Stock market reaction to
inflation announcement in the Indian stock market: A sectoral analysis, Cogent Economics &
Finance, 8:1, 1723827, DOI: 10.1080/23322039.2020.1723827
To link to this article: https://doi.org/10.1080/23322039.2020.1723827
© 2020 The Author(s). This open access
article is distributed under a Creative
Commons Attribution (CC-BY) 4.0 license.
Published online: 07 Feb 2020.
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FINANCIAL ECONOMICS | RESEARCH ARTICLE
Stock market reaction to inflation announcement
in the Indian stock market: A sectoral analysis
Gurmeet Singh1* and Lakshmi Padmakumari2
Abstract: This study investigates the reaction of stock returns to the inflation
announcement using time series data from 2012 to 2018. To check the market
efficiency or semi-strong efficiency of the Indian Stock Market for inflation
announcement, we have used an event study methodology. We selected nine
events based on consensus estimate and actual inflation number; we put events
into subgroups based on over-estimation, under-estimation, and accurate estimation. We performed an event study on inflation-sensitive sectors such as Banking,
Energy, Realty, Service, and FMCG. To check for the above objectives, we calculated
Average Abnormal Return (AAR), Cumulative Abnormal Return (CAR), and
Cumulative Average Abnormal Return (CAAR). The finding of the study suggests that
there are considerable abnormal returns, which are a function of the sector and the
regime. Some sectors are more sensitive to inflation announcements, and some
regimes are again more sensitive to inflation announcements.
Subjects: Economic Theory & Philosophy; Macroeconomics; Corporate Finance
Keywords: inflation; event studies; market efficiency
JEL Classification: E31; G41
1. Introduction
Numerous factors influence the stock markets, such as domestic and foreign news. Some news or
announcements are firm-specific or affect a particular sector, whereas others affect the market as
a whole like inflation, GDP growth, the repo rate, to name a few. Inflation is one of the macroeconomic factors that could influence the stock market (Corrado & Jordan,
2002;). Inflation is the
ABOUT THE AUTHORS
Gurmeet Singh is a Ph.D. Scholar in Department
of Finance, Institute for Financial Management
and Research affiliated to University of Madras.
His research interest is in Corporate Finance,
Macroeconomics and Derivatives.
Lakshmi Padmakumari is currently working as
an Assistant Professor, Department of Finance
with IFMR Graduate School of Business, Krea
University. She is a Gold Medalist & University
First Rank holder in her bachelor
s and masters
program. Her research interests mainly include
in the areas of asset pricing, volatility modeling,
financial econometrics and risk management.
She has participated and presented her research
papers at multiple conferences both in India and
abroad. She can be reached at
[email protected]
PUBLIC INTEREST STATEMENT
The paper studies the market reaction of stock
market to inflation announcement across the
inflation switching regime. We study the five
sectors namely, banking, realty, energy, services,
and housing and check which sector is more
sensitive to inflation announcements. Out of 88
events we select 9 random events based on
consensus estimate and actual inflation to test
the stock market efficiency for inflation
announcement. Using event study methodology
we found that there are significant AAR and CAR
which is function of the sector and regime. Hence,
markets are not efficient in the short run.
However, the inflation effect seemed to dying as
India moved from WPI to CPI and later when it
adopted IT.
Singh & Padmakumari, Cogent Economics & Finance (2020), 8: 1723827
https://doi.org/10.1080/23322039.2020.1723827
© 2020 The Author(s). This open access article is distributed under a Creative Commons
Attribution (CC-BY) 4.0 license.
Received: 25 April 2019
Accepted: 27 January 2020
*Corresponding author: Gurmeet
Singh, Finance, IFMR, No. 196, TTK
Road, Parthasarathy Gardens,
Alwarpet, Chennai 600018, India
E-mail:
[email protected]
Reviewing editor:
David McMillan, University of Stirling,
Stirling, UK
Additional information is available at
the end of the article
Page 1 of 22
Table 1. Results of the empirical analysis
BANK

E.1A-W E.4U-C E.7U-IT
Date ACAR tval AAR tval Date ACAR Tval AAR tval Date ACAR tval AAR tval
5 0.010** 2.21 0.010** 2.21 5 0.008*** 4.92 0.008*** 4.92 5 0.005*** 3.75 0.005*** 3.75
4 0.003 0.49 0.007 1.42 4 0.009*** 3.06 0.001 0.37 4 0.001 0.17 0.004 1.05
3 0.010* 1.83 0.007** 1.99 3 0.012*** 2.57 0.003 0.75 3 0.001 0.17 0.001 0.51
2 0.019** 2.65 0.009* 1.94 2 0.015*** 2.57 0.003 1.13 2 0.002 0.43 0.001 0.52
1 0.019* 1.64 0.000 0.05 1 0.015** 2.22 0.001 0.20 1 0.003 0.48 0.001 0.32
0 0.023*** 3.03 0.004 0.79 0 0.021** 2.45 0.007** 2.32 0 0.001 0.17 0.002 0.56
1 0.033*** 3.88 0.010* 1.9 1 0.039*** 3.69 0.017*** 3.69 1 0.008 1.10 0.007 1.63
2 0.037*** 4.01 0.004 0.46 2 0.041*** 3.82 0.002 0.60 2 0.006 0.50 0.015 1.34
3 0.037*** 2.90 0.000 0.08 3 0.035*** 3.28 0.006 1.52 3 0.011 0.75 0.005 1.14
4 0.036*** 2.83 0.001 0.9 4 0.027** 2.37 0.008* 1.88 4 0.003 0.23 0.008* 1.95
5 0.010 0.80 0.026*** 3.3 5 0.027* 1.87 0.001 0.19 5 0.002 0.11 0.001 0.28
E.2U-W E.5A-C E.8A-IT
T ACAR tval AAR tval T ACAR Tval AAR tval T ACAR tval AAR tval
5 0.000 0.47 0.000 0.47 5 0.002 0.46 0.002 0.46 5 0.009 1.53 0.009 1.53
4 0.009*** 2.67 0.009*** 3.16 4 0.001 0.31 0.003 1.48 4 0.005 0.84 0.003 1.19
3 0.010* 1.87 0.001 0.40 3 0.004 0.73 0.005 1.50 3 0.019** 2.02 0.014*** 3.23
2 0.014*** 2.74 0.004* 1.81 2 0.012** 2.15 0.008*** 2.77 2 0.045*** 2.77 0.026*** 2.84
1 0.014*** 2.67 0.000 0.22 1 0.022*** 3.27 0.010** 2.46 1 0.057*** 3.19 0.013** 2.38
0 0.003 0.49 0.011*** 2.75 0 0.025*** 2.79 0.003 1.09 0 0.062** 2.02 0.004 0.30
1 0.030*** 2.72 0.034*** 4.16 1 0.028** 2.20 0.003 0.48 1 0.048* 1.64 0.014*** 2.76
2 0.044*** 2.71 0.013 1.46 2 0.021 1.57 0.007** 2.11 2 0.049 1.61 0.001 0.24
3 0.045*** 2.68 0.001 0.34 3 0.016 1.22 0.005** 2.47 3 0.047 1.43 0.002 0.62

(Continued)
Singh & Padmakumari, Cogent Economics & Finance (2020), 8: 1723827
https://doi.org/10.1080/23322039.2020.1723827
Page 2 of 22

Table1. (Continued)

BANK

E.1A-W E.4U-C E.7U-IT
Date ACAR tval AAR tval Date ACAR Tval AAR tval Date ACAR tval AAR tval
4 0.057*** 3.14 0.012** 2.36 4 0.013 0.92 0.003 1.56 4 0.027 0.91 0.020** 2.24
5 0.064*** 4.19 0.007* 1.66 5 0.010 0.69 0.004 0.91 5 0.028 0.99 0.001 0.20
E.3O-W E.6O-C E.9O-IT
Date ACAR tval AAR tval Date ACAR Tval AAR tval Date ACAR tval AAR tval
5 0.012*** 2.83 0.012*** 2.83 5 0.004 1.08 0.004 1.08 5 0.007** 2.03 0.007** 2.03
4 0.010* 1.85 0.002 0.64 4 0.001 0.12 0.004 1.37 4 0.009 1.53 0.002 0.63
3 0.009 1.41 0.002 0.41 3 0.012** 2.42 0.011*** 2.71 3 0.015** 2.09 0.006* 1.75
2 0.006 0.79 0.003 0.56 2 0.022*** 3.20 0.010* 1.86 2 0.015** 2.28 0.000 0.14
1 0.016 1.59 0.022*** 6.50 1 0.024*** 3.42 0.002 0.76 1 0.005 0.95 0.010*** 3.64
0 0.017 1.10 0.001 0.19 0 0.040*** 3.93 0.016*** 3.65 0 0.003 0.49 0.002 0.76
1 0.017 1.27 0.000 0.02 1 0.054*** 4.53 0.014*** 3.80 1 0.003 0.51 0.000 0.05
2 0.026* 1.64 0.009** 2.25 2 0.060*** 5.27 0.006 1.13 2 0.003 0.42 0.007 1.01
3 0.034** 2.31 0.008 1.55 3 0.074*** 6.16 0.015*** 4.32 3 0.003 0.34 0.000 0.12
4 0.042*** 3.05 0.008** 2.22 4 0.077*** 5.22 0.003 0.70 4 0.019 1.31 0.016* 1.77
5 0.037** 2.33 0.005 1.08 5 0.079*** 5.22 0.002 0.39 5 0.021 1.48 0.002 1.01

ENERGY

E.1A-W E.4U-C E.7U-IT
Date ACAR tval AAR tval Date ACAR Tval AAR tval Date ACAR tval AAR tval
5 0.002 0.36 0.002 0.36 5 0.013** 2.55 0.013** 2.55 5 0.006*** 3.18 0.006*** 3.18
4 0.010 1.33 0.011* 1.77 4 0.006 0.92 0.007** 2.18 4 0.003 0.58 0.009 1.58
3 0.012 1.53 0.002 0.37 3 0.005 1.12 0.011** 2.13 3 0.002 0.36 0.005 1.45
2 0.013** 1.96 0.001 0.28 2 0.006 0.78 0.011*** 2.89 2 0.012 1.52 0.010*** 2.86

(Continued)
Singh & Padmakumari, Cogent Economics & Finance (2020), 8: 1723827
https://doi.org/10.1080/23322039.2020.1723827
Page 3 of 22

Table1. (Continued)

BANK

E.1A-W E.4U-C E.7U-IT
Date ACAR tval AAR tval Date ACAR Tval AAR tval Date ACAR tval AAR tval
1 0.013* 1.68 0.000 0.05 1 0.021** 2.25 0.015** 2.56 1 0.015* 1.91 0.003 1.04
0 0.008 0.65 0.006 1.08 0 0.022** 2.56 0.002 0.26 0 0.017** 2.49 0.002 0.72
1 0.006 0.58 0.001 0.22 1 0.025** 2.08 0.003 0.49 1 0.017** 2.44 0.000 0.00
2 0.009 0.62 0.003 0.38 2 0.024** 1.96 0.001 0.20 2 0.027*** 3.85 0.010*** 7.08
3 0.007 0.51 0.002 0.33 3 0.014 0.90 0.010 1.51 3 0.026*** 3.54 0.000 0.07
4 0.006 0.53 0.001 0.15 4 0.007 0.37 0.008** 2.12 4 0.025*** 3.59 0.001 0.25
5 0.012 1.00 0.005 0.97 5 0.005 0.35 0.001 0.27 5 0.024*** 3.19 0.001 0.34
E.2U-W E.5A-C E.8A-IT
Date ACAR tval AAR tval Date ACAR Tval AAR tval Date ACAR tval AAR tval
5 0.001 0.87 0.001 2.74 5 0.003 0.81 0.003 0.81 5 0.001 0.29 0.001 0.29
4 0.000 0.16 0.001 0.87 4 0.007 0.76 0.010 1.47 4 0.001 0.14 0.000 0.12
3 0.002 0.47 0.003** 2.08 3 0.002 0.24 0.005* 1.65 3 0.008 0.99 0.008 1.49
2 0.008 1.56 0.010*** 8.80 2 0.005 0.75 0.007 1.49 2 0.017** 2.11 0.008* 1.75
1 0.002 0.39 0.006*** 7.58 1 0.013 1.34 0.007 1.39 1 0.021*** 2.67 0.004 1.03
0 0.019** 2.20 0.017*** 8.53 0 0.002 0.21 0.011*** 3.34 0 0.022 1.51 0.001 0.10
1 0.020 1.52 0.001 0.23 1 0.012 1.11 0.010*** 3.15 1 0.019 1.32 0.003 0.60
2 0.020 1.43 0.000 0.33 2 0.003 0.34 0.016*** 2.80 2 0.028* 1.73 0.009** 2.54
3 0.018 1.50 0.002 1.16 3 0.010 0.90 0.007 1.55 3 0.039** 2.44 0.011* 1.79
4 0.006 0.44 0.012*** 9.96 4 0.018 1.62 0.008*** 3.03 4 0.038** 2.54 0.001 0.14
5 0.005 0.26 0.001 0.75 5 0.031** 2.19 0.012** 2.53 5 0.031** 2.30 0.007** 2.14

(Continued)
Singh & Padmakumari, Cogent Economics & Finance (2020), 8: 1723827
https://doi.org/10.1080/23322039.2020.1723827
Page 4 of 22

Table1. (Continued)

BANK

E.1A-W E.4U-C E.7U-IT
Date ACAR tval AAR tval Date ACAR Tval AAR tval Date ACAR tval AAR tval
E.3O-W E.6O-C E.9O-IT
Date ACAR tval AAR tval Date ACAR Tval AAR tval Date ACAR tval AAR tval
5 0.013*** 3.06 0.013*** 3.06 5 0.000 0.02 0.000 0.02 5 0.001 0.25 0.001 0.25
4 0.008 1.54 0.005 1.49 4 0.017*** 4.36 0.018*** 4.58 4 0.011* 1.78 0.010** 2.20
3 0.002 0.28 0.006 1.19 3 0.044*** 4.70 0.027*** 3.76 3 0.003 0.34 0.008** 2.22
2 0.010 1.50 0.013*** 4.92 2 0.027*** 4.56 0.017*** 3.89 2 0.002 0.28 0.001 0.19
1 0.016** 1.76 0.026*** 6.33 1 0.043*** 5.89 0.015*** 4.86 1 0.008 1.17 0.006 0.96
0 0.028** 2.41 0.012* 1.78 0 0.042*** 5.15 0.001 0.29 0 0.003 0.31 0.005 1.35
1 0.024*** 2.67 0.004 0.93 1 0.041*** 5.68 0.000 0.08 1 0.005 0.69 0.008*** 2.57
2 0.011 1.29 0.012*** 2.77 2 0.052*** 6.39 0.011* 1.88 2 0.009 1.16 0.003 1.18
3 0.006 0.56 0.006 1.46 3 0.053**** 6.89 0.000 0.13 3 0.005 0.63 0.004 1.11
4 0.008 0.62 0.003 0.47 4 0.060*** 5.19 0.008 1.25 4 0.009 0.94 0.013** 2.21
5 0.006 0.48 0.002 0.66 5 0.057*** 6.35 0.003 0.33 5 0.005 0.58 0.003* 1.74

FMCG

E.1A-W E.4U-C E.7U-IT
Date ACAR tval AAR tval Date ACAR Tval AAR tval Date ACAR tval AAR tval
5 0.008 1.25 0.008 1.25 5 0.001 0.39 0.001 0.39 5 0.000 0.00 0.000 0.00
4 0.009 1.26 0.001 0.14 4 0.003 0.62 0.002 0.37 4 0.015** 2.19 0.014*** 2.73
3 0.003 0.29 0.006 0.89 3 0.002 0.63 0.005 1.38 3 0.010 0.98 0.005 1.15
2 0.001 0.09 0.002 0.37 2 0.013** 2.49 0.011** 2.49 2 0.010 0.79 0.000 0.02
1 0.005 0.40 0.004 0.87 1 0.008 0.99 0.005 0.99 1 0.011 0.90 0.002 0.68
0 0.002 0.19 0.003 0.87 0 0.012 1.04 0.004 0.85 0 0.004 0.34 0.007 1.22

(Continued)
Singh & Padmakumari, Cogent Economics & Finance (2020), 8: 1723827
https://doi.org/10.1080/23322039.2020.1723827
Page 5 of 22

Table1. (Continued)

BANK

E.1A-W E.4U-C E.7U-IT
Date ACAR tval AAR tval Date ACAR Tval AAR tval Date ACAR tval AAR tval
1 0.003 0.24 0.001 0.19 1 0.012 0.89 0.001 0.09 1 0.012 0.82 0.008* 1.79
2 0.001 0.12 0.004 0.99 2 0.003 0.20 0.009* 1.88 2 0.011 0.68 0.002 0.45
3 0.002 0.16 0.004 0.84 3 0.005 0.27 0.008 1.16 3 0.008 0.51 0.002 0.60
4 0.002 0.10 0.004 0.53 4 0.015 0.81 0.010*** 3.20 4 0.006 0.37 0.002 0.59
5 0.008 0.35 0.009 1.00 5 0.013 0.84 0.002 0.32 5 0.005 0.30 0.001 0.46
E.2U-W E.5A-C E.8A-IT
Date ACAR tval AAR tval Date ACAR Tval AAR tval Date ACAR tval AAR tval
5 0.000 0.13 0.000 0.13 5 0.002 0.71 0.002 0.71 5 0.010*** 2.67 0.010*** 2.67
4 0.003 0.75 0.004 0.91 4 0.001 0.14 0.003 0.65 4 0.017*** 3.27 0.007** 2.19
3 0.007 0.79 0.004 0.69 3 0.001 0.18 0.002 0.70 3 0.026*** 5.16 0.009** 2.25
2 0.016 1.44 0.009** 2.29 2 0.010 1.09 0.008* 1.87 2 0.032*** 5.72 0.006* 1.65
1 0.006 0.73 0.010 1.21 1 0.000 0.03 0.010*** 3.16 1 0.055*** 8.78 0.023*** 3.85
0 0.026*** 2.74 0.020*** 4.02 0 0.003 0.25 0.003 0.76 0 0.073*** 6.41 0.018*** 2.75
1 0.038*** 3.46 0.012 1.60 1 0.008 0.53 0.005 1.08 1 0.068*** 5.62 0.005 1.04
2 0.036*** 3.41 0.002 0.49 2 0.005 0.31 0.003 0.66 2 0.063*** 5.27 0.005 1.39
3 0.041*** 4.15 0.005 1.63 3 0.003 0.20 0.008** 2.43 3 0.067*** 5.92 0.004 1.64
4 0.039*** 3.79 0.002 0.56 4 0.006 0.43 0.003 0.61 4 0.065*** 6.12 0.002 0.54
5 0.042*** 3.17 0.003 0.43 5 0.018 1.28 0.012** 2.09 5 0.062*** 5.18 0.003 0.73
E.3O-W E.6O-C E.9O-IT
Date ACAR tval AAR tval Date ACAR Tval AAR tval Date ACAR tval AAR tval
5 0.008* 1.93 0.008* 1.93 5 0.005 1.16 0.005 1.16 5 0.008* 1.88 0.008* 1.88
4 0.006 1.01 0.002 0.58 4 0.007 1.28 0.002 0.44 4 0.008 1.23 0.000 0.01

(Continued)
Singh & Padmakumari, Cogent Economics & Finance (2020), 8: 1723827
https://doi.org/10.1080/23322039.2020.1723827
Page 6 of 22

Table1. (Continued)

BANK

E.1A-W E.4U-C E.7U-IT
Date ACAR tval AAR tval Date ACAR Tval AAR tval Date ACAR tval AAR tval
3 0.007 0.91 0.013*** 4.09 3 0.019*** 3.37 0.012*** 3.96 3 0.009** 2.12 0.000 0.09
2 0.006 0.65 0.001 0.26 2 0.024*** 4.24 0.005** 2.13 2 0.011* 1.72 0.002 0.62
1 0.008 0.79 0.014*** 3.66 1 0.036*** 5.37 0.011*** 3.97 1 0.009** 2.07 0.002 0.53
0 0.003 0.22 0.005 1.03 0 0.039*** 4.81 0.003 1.04 0 0.014*** 3.66 0.005 1.44
1 0.007 0.51 0.010** 2.24 1 0.042*** 5.24 0.003 0.63 1 0.009* 1.77 0.005** 2.40
2 0.001 0.06 0.006 1.32 2 0.036*** 3.89 0.006 1.42 2 0.005 0.66 0.004 1.04
3 0.008 0.50 0.007** 1.98 3 0.034*** 3.38 0.002 0.43 3 0.003 0.35 0.002 0.55
4 0.010 0.53 0.002 0.38 4 0.036*** 3.70 0.002 0.76 4 0.003 0.36 0.006 1.08
5 0.017 0.88 0.007** 2.27 5 0.028** 2.60 0.008** 2.53 5 0.001 0.09 0.004* 1.66

Realty

E.1A-W E.4U-C E.7U-IT
Date ACAR tval AAR tval Date ACAR tval AAR tval Date ACAR tval AAR tval
5 0.013 1.60 0.013 1.59 5 0.007 0.83 0.007 0.83 5 0.001 0.05 0.001 0.05
4 0.018 1.39 0.031** 2.56 4 0.011 1.00 0.003 0.38 4 0.001 0.05 0.001 0.17
3 0.035** 2.05 0.017 0.97 3 0.006 0.45 0.005 0.96 3 0.008 0.44 0.008** 2.08
2 0.032 1.57 0.003 0.15 2 0.020 1.22 0.025*** 5.59 2 0.006 0.28 0.002 0.33
1 0.031 1.60 0.001 0.05 1 0.032* 1.88 0.012** 2.01 1 0.015 0.67 0.010 1.29
0 0.061** 2.17 0.030 1.11 0 0.045*** 2.57 0.013* 1.95 0 0.011 0.49 0.004 1.02
1 0.062** 2.38 0.002 0.07 1 0.098*** 4.31 0.053*** 2.92 1 0.013 0.64 0.002 0.45
2 0.087** 2.52 0.024 0.74 2 0.091*** 4.32 0.007 0.93 2 0.010 0.38 0.003 0.46
3 0.080** 2.39 0.007 0.22 3 0.096*** 4.06 0.005 0.61 3 0.007 0.23 0.003 0.64
4 0.107*** 3.02 0.027 0.80 4 0.087*** 3.10 0.009 1.05 4 0.015 0.46 0.022*** 3.03

(Continued)
Singh & Padmakumari, Cogent Economics & Finance (2020), 8: 1723827
https://doi.org/10.1080/23322039.2020.1723827
Page 7 of 22

Table1. (Continued)

BANK

E.1A-W E.4U-C E.7U-IT
Date ACAR tval AAR tval Date ACAR Tval AAR tval Date ACAR tval AAR tval
5 0.069** 2.04 0.038 1.16 5 0.079*** 3.12 0.008 1.69 5 0.024 0.68 0.009** 2.10
E.2U-W E.5A-C E.8A-IT
Date ACAR tval AAR tval Date ACAR tval AAR tval Date ACAR tval AAR tval
5 0.001 0.52 0.001 0.52 5 0.005 0.60 0.005 0.60 5 0.000 0.08 0.000 0.08
4 0.003 0.73 0.004 0.86 4 0.011 1.19 0.006* 1.64 4 0.006 0.98 0.006* 1.80
3 0.002 0.18 0.005 0.63 3 0.004 0.33 0.007 1.05 3 0.078*** 3.65 0.072*** 3.22
2 0.005 0.44 0.003 0.89 2 0.012 0.85 0.008 1.28 2 0.083*** 5.42 0.005 0.55
1 0.000 0.03 0.005 1.04 1 0.014 1.03 0.002 0.43 1 0.082*** 5.23 0.001 0.07
0 0.009 0.60 0.008 1.53 0 0.014 0.78 0.000 0.00 0 0.113*** 5.38 0.031** 2.22
1 0.066*** 3.65 0.058*** 4.65 1 0.025 1.07 0.010 1.41 1 0.115*** 7.14 0.002 0.16
2 0.083*** 4.02 0.017** 2.46 2 0.012 0.60 0.036*** 4.69 2 0.111*** 6.66 0.003 0.28
3 0.085*** 3.67 0.003 0.33 3 0.006 0.29 0.006 1.41 3 0.104*** 5.78 0.007 1.15
4 0.089*** 3.40 0.003 0.37 4 0.034 1.55 0.040*** 2.93 4 0.119*** 5.94 0.015* 1.77
5 0.119*** 4.94 0.031*** 3.16 5 0.032 1.62 0.003 0.36 5 0.097*** 4.49 0.022** 2.16
E.3O-W E.6O-C E.9O-IT
Date ACAR tval AAR tval Date ACAR tval AAR tval Date ACAR tval AAR tval
5 0.003 0.69 0.003 0.69 5 0.003 0.56 0.003 0.56 5 0.014 1.49 0.014 1.49
4 0.011*** 2.60 0.008** 2.01 4 0.027*** 3.33 0.030*** 3.64 4 0.001 0.07 0.014** 2.38
3 0.002 0.59 0.013** 2.20 3 0.032*** 3.25 0.005 0.54 3 0.029* 1.79 0.028* 1.79
2 0.004 0.39 0.002 0.20 2 0.027** 2.51 0.005 1.12 2 0.014 0.99 0.015*** 2.69
1 0.011 0.87 0.015** 2.03 1 0.037*** 3.27 0.010* 1.76 1 0.000 0.01 0.014** 2.35
0 0.013 1.02 0.002 0.26 0 0.024*** 3.03 0.013* 1.79 0 0.003 0.23 0.003 0.82

(Continued)
Singh & Padmakumari, Cogent Economics & Finance (2020), 8: 1723827
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Table1. (Continued)

BANK

E.1A-W E.4U-C E.7U-IT
Date ACAR tval AAR tval Date ACAR Tval AAR tval Date ACAR tval AAR tval
1 0.004 0.25 0.017*** 2.65 1 0.016 0.45 0.040 1.21 1 0.009 0.40 0.006 0.46
2 0.010 0.71 0.014* 1.94 2 0.001 0.05 0.017 1.60 2 0.008 0.28 0.001 0.06
3 0.006 0.42 0.016*** 2.83 3 0.004 0.14 0.003 0.50 3 0.005 0.20 0.003 0.47
4 0.021 1.21 0.027*** 2.67 4 0.018 0.65 0.022** 2.07 4 0.004 0.13 0.002 0.43
5 0.027 1.21 0.006 0.59 5 0.004 0.15 0.014 1.60 5 0.009 0.32 0.013*** 4.59

Services

E.1A-W E.4U-C E.7U-IT
Date ACAR tval AAR tval Date ACAR tval AAR tval Date ACAR tval AAR tval
5 0.005*** 7.43 0.005*** 7.43 5 0.006*** 3.89 0.006*** 3.89 5 0.007** 2.37 0.007** 2.37
4 0.004*** 4.66 0.000 0.58 4 0.003 0.87 0.004 1.42 4 0.005 1.24 0.002 0.81
3 0.001 0.64 0.004*** 5.93 3 0.005 1.45 0.002 1.16 3 0.006 1.17 0.001 0.31
2 0.004*** 3.72 0.005*** 9.06 2 0.008** 1.99 0.003 1.27 2 0.009 1.47 0.003 1.54
1 0.002 1.18 0.003*** 4.62 1 0.010** 2.09 0.003 0.99 1 0.007 1.04 0.001 0.43
0 0.001 0.39 0.002*** 4.42 0 0.017*** 3.05 0.006** 2.15 0 0.003 0.47 0.004 1.61
1 0.004** 2.45 0.004*** 7.77 1 0.026*** 3.55 0.010*** 2.89 1 0.001 0.09 0.003 1.03
2 0.008*** 5.68 0.005*** 6.59 2 0.030*** 4.48 0.003 1.34 2 0.008 1.02 0.008* 1.87
3 0.011*** 6.40 0.003*** 5.29 3 0.030*** 4.85 0.000 0.11 3 0.012 1.34 0.004* 1.71
4 0.011*** 6.24 0.000 0.44 4 0.026*** 4.01 0.004 1.37 4 0.008 0.98 0.004* 1.72
5 0.004** 2.24 0.015*** 16.88 5 0.022*** 3.05 0.001* 1.68 5 0.012 1.24 0.004 1.42
E.2U-W E.5A-C E.8A-IT
Date ACAR tval AAR tval Date ACAR tval AAR tval Date ACAR tval AAR tval
5 0.002 13.70 0.002*** 13.70 5 0.001 0.31 0.001 0.31 5 0.002 0.56 0.002 0.56

(Continued)
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Table1. (Continued)

BANK

E.1A-W E.4U-C E.7U-IT
Date ACAR tval AAR tval Date ACAR Tval AAR tval Date ACAR tval AAR tval
4 0.004*** 9.61 0.002*** 4.83 4 0.003 0.99 0.004 1.24 4 0.002 0.49 0.003 1.54
3 0.006*** 8.95 0.002*** 3.63 3 0.002 0.53 0.005* 1.92 3 0.003 0.41 0.001 0.22
2 0.009*** 10.78 0.003*** 5.82 2 0.004 1.19 0.002 0.90 2 0.008 0.66 0.011** 1.96
1 0.009*** 11.07 0.000 1.30 1 0.011** 2.56 0.007*** 2.58 1 0.005 0.38 0.002 0.72
0 0.012*** 12.28 0.003*** 4.73 0 0.011** 2.05 0.001 0.45 0 0.008 0.43 0.003 0.39
1 0.001 0.83 0.010*** 10.15 1 0.012* 1.91 0.002 0.66 1 0.016 0.98 0.008 1.55
2 0.005*** 2.82 0.007*** 7.49 2 0.005 0.65 0.008*** 3.33 2 0.014 0.82 0.001 0.51
3 0.008*** 4.13 0.003*** 5.74 3 0.004 0.62 0.001 0.30 3 0.015 0.82 0.001 0.25
4 0.007*** 3.47 0.001 1.08 4 0.003 0.43 0.001 0.38 4 0.005 0.30 0.010** 2.24
5 0.015*** 6.59 0.007*** 10.32 5 0.008 1.28 0.006* 1.87 5 0.006 0.37 0.001 0.41
E.3O-W E.6O-C E.9O-IT
Date ACAR tval AAR tval Date ACAR tval AAR tval Date ACAR tval AAR tval
5 0.002*** 3.38 0.002*** 3.38 5 0.001 0.23 0.001 0.23 5 0.003 1.25 0.003 1.25
4 0.007*** 6.11 0.005*** 4.97 4 0.001 0.26 0.000 0.10 4 0.005 1.52 0.003 1.16
3 0.003*** 2.91 0.003*** 6.89 3 0.002 0.57 0.003 0.79 3 0.006* 1.65 0.001 0.38
2 0.006*** 5.34 0.003*** 4.50 2 0.000 0.04 0.003 0.84 2 0.007* 1.94 0.001 0.70
1 0.001 0.57 0.005*** 2.88 1 0.001 0.14 0.001 0.21 1 0.005 1.22 0.002 0.64
0 0.007*** 3.47 0.006*** 9.11 0 0.008 1.13 0.007*** 2.87 0 0.003 0.77 0.002 1.08
1 0.014*** 6.03 0.006*** 9.38 1 0.018** 2.04 0.010*** 3.18 1 0.004 0.69 0.001 0.21
2 0.016*** 6.43 0.002*** 4.04 2 0.013 1.17 0.005 1.39 2 0.001 0.07 0.003 0.90
3 0.013*** 4.95 0.003*** 4.08 3 0.013 0.92 0.000 0.00 3 0.004 0.34 0.003 0.85
4 0.014*** 4.37 0.001 1.18 4 0.013 0.85 0.000 0.02 4 0.000 0.00 0.003 0.78
5 0.017*** 5.07 0.002*** 5.09 5 0.018 1.18 0.006* 1.86 5 0.001 0.05 0.001 0.29

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increase in the price of goods and services over a period of time. An increase in prices decreases
the purchasing power of money and the value of the financial asset. India is an economy that has
lived with inflation throughout its history. India
s inflation was on the rise over the last decade;
however, the numbers have started to change since 2010. The Indian economy is doing well post
2010, where it had a 10% inflation level, and now it is below 4%. Over the years, there have been
numerous studies analyzing the effect of inflation and related macro-economic events on the
financial market. Some studies, such as Fama and William Schwert (
1977), Schwert (1981) and
Fama (
1981) found a negative correlation between the stock market and inflation. However, some
studies, such as Pearce and Roley (
1985) and Hardouvelis (1988), found very little or no significant
relationship between inflation and the stock market. For India, Chatrath et al. (
1997) found stock
returns to be a perfect hedge for the expected component of inflation but showed a negative
relationship for the unexpected component of inflation. Our paper attempts to study the market
reaction of inflation announcement on stock prices and find out how the relationship holds for
India, as there are very few studies on the emerging markets.
The Indian economy had moved from Wholesale price index (WPI) to Consumer price index (CPI)
in 2014 and then went on to adopt inflation targeting in 2015. Wholesale price index (WPI) and the
Consumer price index (CPI) are two sources of information to detect inflation. Consumer price
index (CPI) is a measure that calculates inflation based on the weighted average of prices of goods
and services, such as petrol, food, clothing, medical care, and cars. The wholesale price index (WPI)
is an indicator of price changes in the wholesale market. It calculates the prices paid by the
manufacturers and wholesalers in the market. The composition of both WPI and CPI is different, as
shown in
Figure A1 (Please refer to Appendix A); hence, both reports different inflation numbers. By
inflation targeting, the central bank (RBI) sets a specific interest rate as the goal. It tries to achieve
it through various measures such as interest rate, reserve requirement and changing the money
supply. Since the service sector accounts for 60% GDP, it was necessary to look at CPI to get a
better picture of the economy as the weight of services is more in CPI. CPI is also a better measure
of monetary policy decisions and interest rate decisions. India adopted inflation targeting because
it offers a framework to achieve a specific inflation rate credibly and sustainably. Inflation targeting also imposes discipline on monetary policies. From the past literature, we can see that there
have been very few studies trying to study the short run response or impact of inflation announcement on stock prices in the inflation switching environment, i.e., from WPI to CPI, and then
inflation targeting.
According to Fama (
1970), markets are efficient when stock prices reflect all available
information. Ross, Westerfield, and Jaffe (
1999) argue that since stock prices reflect all the
information, an investor or trader should only get a normal rate of return, and no abnormal
return should be present. Markets are efficient when there is no abnormal return, and stock
prices reflect all available information. Indian markets are efficient to inflation announcement if that announcement can
t be used to earn an abnormal return. It would be interesting to see if stock markets reflect the information content of the inflation announcement
across the inflation switching regime. In this study, we have used an event study methodology to analyze the effect of inflation on stock prices using Nifty 50 as the market index.
There are nine events in total, three during the Wholesale price index (WPI) regime, three
during the Consumer price index (CPI) regime, and three when India adopted Inflation
targeting. The objective of the study is to investigate the short-run effect of inflation
announcement on stock prices for various sectors during the event window though stock
market reaction. There are limited studies that investigate the impact of inflation on various
sectors in an inflation switching environment, i.e., from Wholesale price index (WPI) to
Consumer price index (CPI) and later to inflation targeting. We have selected five inflationsensitive sectors, namely, Banking, Energy, Realty, FMCG, and Services. Our study has added
to the existing literature by investigating the short-run response of daily stock prices on the
Indian market to inflation announcements in an inflation switching environment across
different sectors, within the framework of an emerging market like India. The rest of the
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paper is organized as follows; section 2 reviews the existing literature related to the impact
of inflation on stock prices, section
3 describes the data and methodology used, section 4
details the results and analysis and section 5 gives the conclusion, limitation of our study
and scope for future research.
2. Literature review
According to economic theory, the nominal return is the sum of expected inflation and real
return. So, the stock return should be positively related to inflation, assuming inflation and
real return are independent. Fisher (
1930) expected nominal return of stock to be equal to
expected inflation plus the real rate of return. Fisher
s hypothesis also predicts a positive
relationship between stock return and inflation. According to the Fisher Hypothesis, the stock
market should act as a hedge against inflation. Another school of thought (Fama Proxy
Hypothesis,
1981) says that the stock market is not affected by inflation. However, there has
been empirical work showing changes in the expected inflation are negatively affecting the
stock return. Nelson (
1976), Jaffe and Mandelker (1976), Bodie (1976), Fama and William
Schwert (
1977), Modiglian and Cohn (1979), Geske and Roll (1983) and Kaul (1987) supported
the negative relationship between stock return and inflation. Several reasons put forth for
this negative relationship was taxation-relationship, dividend price ratios, and price-earnings
ratios, negative relationship between inflation and real economic activity, etc.
Díaz and Jareño (
2009) studied the short-run effect of inflation announcement on stock prices in
various sectors. They used an event study methodology to investigate the relation between unanticipated inflation announcements and stock returns. There was no evidence of a significant relationship
between abnormal return and inflation announcement. Adams, McQueen, and Wood (
2004) studied
the intraday stock return for PPI and CPI and find that the news response is strong when the economy
is strong and the news is bad. Schwert (
1981) examined the daily returns of Standards and Poors
around the CPI announcement and observed negative market reaction for unanticipated inflation
component in CPI. Jain (
1988) also found a negative effect on stock prices and trading volume using
hourly data for the CPI announcement.
According to Knif, Kolari, and Pynnönen (
2008), stock market reaction can provide a different
result for both positive and negative inflation shock depending on the state of the economy, i.e.,
the effect of inflation on stock returns depends on how investors perceive inflation in different
economic state. Wei (
2009) further supported the study and found equity returns respond negatively to unexpected inflation during contraction than expansion.
Khil and Lee (
2000) checked for real stock return and inflation relationship for the U.S. and 10
Pacific Rim countries for the sample period from 1970 to 1997. They found a negative real stock
return and inflation correlation for 9 Pacific Rim countries and the U.S. However, Malaysia showed
a positive relationship between real stock return and inflation. The reason for checking pacific and
the U.S. was because of the inflation rate. For example, the U.S. experiences mild inflation, whereas
the Asian countries experience very high inflation. Pimentel and Choudhry (
2014) provide empirical
evidence of the positive relationship between composite stock returns and inflation for Brazil
during periods of high inflation.
Tiwari, Dar, Bhanja, Arouri, and Teulon (
2015) found stock returns and inflation to be positively
related to CPI and independent for PPI (Producers Price Index). Overall, using both measures, they
also found that in the long run, stocks could be used as a hedge against inflation in Pakistan. One
of the more recent studies by Antonakakis et al. (
2017) examined the dynamic conditional
correlation of stock prices and inflation in the U.S. over the period of 1791
2015. They observed
the correlation between inflation and stock prices evolve heterogeneously over time. In particular,
the correlation is significantly positive in the 1840s, 1860s, 1930s, and 2011 and significantly
negative for other time periods.
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The existing literature includes three groups of studies: short-run response of inflation
announcement on stock returns, short-run relationship, and long-run relationship. To sum up,
empirical literature provides a mixed conclusion on the relationship between inflation and stock
returns. Further, it would be interesting to see the market reaction of inflation announcement in a
country like India behaves, which has moved from WPI in 2014 to CPI and later adopted inflation
targeting in 2016 for five years, which is supposed to end in 2021. It would also be interesting to
check if the source of inflation number matter along with inflation targeting in determining the
relationship between inflation and stock returns?
Our study adds to the existing literature by checking the short-run response of stock return to
inflation announcement in the inflation switching environment across sectors. Here, we study the
impact of inflation on various inflation-sensitive sectors like FMCG, bank, realty, services, etc. in a
regime-switching environment to find out which sector is more sensitive to inflationary announcement when compared to others. We have used event study methodology, and the events were
selected based on the actual inflation and forecasted inflation. Our paper has one more distinct
feature in comparison to the existing work. We have also used several events as compared to the
existing works where the study was based on only a few events. We selected the events based on
consensus estimates and actual inflation.
3. Data and methodology
We have used an event study to understand the relationship between inflation & stock market
returns. With an event study, one can find the effect of a particular event (MacKinlay,
1997). Event
study has been widely used to check the stock price movement around an event (Ashley
1962; Ball
& Brown,
1968; Barker, 1956; Dolley, 1933; Fama, 1981; Fama, Fisher, Jensen, & Roll, 1969; Myers &
Bakay,
1948; Watts 1978). We have also followed the same approach and used the event study
approach to measure the impact of inflation announcement on stock prices and thereby check
market efficiency. By market efficiency, we mean, stock prices should reflect all available information that is publicly and privately available, which means there should be no abnormal returns.
For conducting the event study, we have used three sets of data. Firstly, we used the inflation
announcements measured in terms of WPI and CPI (actual and forecast). The actual inflation
number is the reported number, and the forecast is a consensus estimate based on the combined
estimate of analysts covering the inflation data. The inflation announcement data is taken from
https://in.investing.com/economic-calendar/indian-cpi-973. Secondly, we took the closing price of
each stock from the respective sectoral indices (Banking NIFTY, Energy NIFTY, Realty NIFTY,
Services NIFTY, and FMCG NIFTY). Thirdly, we used the closing price of the Nifty 50 index. In this
study, we had a total of 88 sample events for inflation announcement, out of which we randomly
selected nine events based on the over-estimation, under-estimation, and accurate. There is overestimation when forecasted inflation is more than the actual inflation and under-estimation when
actual inflation is more than the forecasted inflation. An event is accurate when actual inflation is
equal to forecasted inflation.
The rationale behind this approach is that the unexpected component contains new information,
which is not reflected in the stock prices. Over here, unexpected would mean over and underestimation, and hence it would be interesting to see the changes in stock prices around the event.
The stock prices and Nifty 50 data are from the Prowess database. The events selected for the
study are reported in Table
B1 (Please refer to Appendix B). The timeline used for the event study
includes the event date (announcement date), test period or event window, and estimation
window. As explained below, the event date is day 0, the estimation period which is ten days
before the event date (
t ¼ 0) is of 90 days from which we estimate alphaand betaas
suggested by Brown and Warner (
1985) and our event window is of 10 days. Alpha is the intercept,
and beta being the slope coefficient. Beta is also stock
s volatility as compared to the market
(Panayides & Gong,
2002).
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To study the effect of inflation announcement on stock returns, we need to look at returns
instead of share prices. Hence, we calculated the log-returns using the formula:

Ri;t ¼ ln
  Pi;t1
Where,
(1)

Pi;t
Pi;t: is the closing price of the security on day t of security i
P
i;t1: is the closing price of the security on day t 1 of security i
t: refers to time
Ri;t: is the return on security i on day t
Now, we try to find out the expected return by using a simple OLS regression analysis, where the
parameters are calculated from the estimation period, which is consistent with the market model
approach (MacKinlay,
1997). Our main assumption to use the market model is that security returns
are a linear function of the market movement. Market model (Strong,
1992) assumes that returns
are generated using the following formula:

E R ð Þi;t ¼ /i;t þ bi;jRm;t (2)
E R ð Þi;t: is the expected return at time t of security i

/i;t and bi;j are the parameters of the regression equation, where alpha (α) is the intercept,
which is nothing but risk -free rate and beta (
β) is slope measuring the sensitivity of stock with the
market.
Rm;t: is the daily return on a stock market index m, at time t
To find out if the stock market is efficient or not, we need to check for abnormal returns around
the event date. The benchmark to calculate abnormal returns is the normal return, which is
calculated from the estimation period. Abnormal return around the event date is the difference
between the actual observed return and the calculated normal return, (
E R ð Þi;tÞ, for each day of the
event window (Seiler,
2004).
Therefore, the abnormal return (AR) of security would be:

ARi;t ¼ Ri;t E R ð Þi;t
ARi;t: is the abnormal return of security i, at time t
(3)

 

ESTIMATION PERIOD EVENT WINDOW

(-)100 (-)10 (-) 5 0 (+) 5
EVENT DATE
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Ri;t: is the actual or observed return of security i, at time t
E R
ð Þi;t: is the expected return on security i, at time t calculated using the market model.
Here, the null hypothesis is: H0 : AR = 0
To better understand the effect of inflation announcement on stock returns, we also calculate
the cumulative average abnormal return by accumulating the abnormal return over the event
window (
t1tot2,) to see if the information is reflected earlier or later and check for buy and hold
strategy by looking ACAR.

CARið Þ ¼ t1; t2 t1;t2ARi (4)
We then calculate the average Cumulative abnormal return (CAR) over the event window (t1tot2Þ:
CAARið Þ ¼ t1; t2 CARi ð Þ t1; t2
N
The significance of CAR and AAR is then tested using a t-stat.
(5)

t stat ¼
CAARið Þ t1; t2
σCARi ð Þ t1;t2
pN
(6)
Larger the t statistic, the less likely that the actual value of the parameter could be 0. That is to
say, a significant t-statistic leads to rejecting the null hypothesis, and it also suggests the existence
of positive or negative abnormal returns owing to inflationary announcements. In this paper, we
see the impact of the inflation announcement on stock returns in the period from July 2011 to
December 2018. The reason for selecting this period is that India moved from WPI to CPI in 2014
and then adopted inflation targeting in 2016. We study five sectors (Banking, Energy, Realty,
Services, and FMCG) based on their sensitivity to inflation data and the weights they got when
calculating the inflation number. We also check for the stock market reaction when India moved
from WPI to CPI and later when it adopted inflation targeting.
4. Empirical results
When markets are efficient, there would be no abnormal return, and prices shouldreflect all the available
information. Hence, it would be impossible to make abnormal returns by using past price and volume
data, or by trading on publicly available information or insider information. It means markets are strong
form efficient. If the event window contains no abnormal return, it means that inflation announcement
news is already reflected in the stock prices, and there is no relationship between abnormal return and
inflation announcement. However, if the prices reflect the inflation announcement news just around the
event date, then it means that there are abnormal returns. It will be interesting to see if the abnormal
return is positive or negative. According to economic literature, stock return should be positively related
to inflation because equities act as a hedge against inflation. This is one of the main reasons why
individuals invest in stocks. Empirical literature finds that inflation is negatively related to stock returns
(Fama & William Schwert,
1977). We examine the impact of each event separately on each sectoral
return. Further, we have divided the events into three groups WPI (wholesale price index), CPI (consumer
price index), and IT (Inflation targeting). Also, based on consensus estimates in each group, we have
accurate events (consensus is equal to actual inflation), overestimation (forecast is more than actual
inflation), and underestimation (forecast is less than actual inflation).
The Table 1 present the AAR and CAR along with their t-statistics over the event window. Before we
discuss the findings and results, it
s essential to know the notations used. The left side events
correspond to WPI (Wholesale Price Index), and we have used W to represent it, the center corresponds to CPI (Consumer Price Index), and we represent the same by C, and the right side events fall
under IT(Inflation targeting) regime. Accurate events are marked as A; Underestimation events are
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marked as U and Overestimation events are marked as O. For example, E.1A-W means the first event is
accurate, and the source of inflation number is WPI.
4.1. Findings and discussion
In the banking sector, out of 9 events, only 2 showed statistically significant ACAR(Average cumulative
abnormal return) and AAR(Average abnormal return) on the event date. One was an underestimation,
which is event 4, which is statistically significant at the 5% level, and the other one was event 6, which
is statistically significant at the 10% level. Both events 4 and 6 are in the CPI regime. However, there
are significant abnormal returns present on days before the announcement and after the announcement for most events. The effect of the inflation announcement seems to be dying as India moved
from WPI to CPI and later to IT.
1 Figure 1 depicts the average cumulative abnormal return over the
event window. As seen on the graph, there are abnormal returns, and the returns continue even after
the announcement date. However, ACAR is most for WPI and CPI as compared to IT. Since the interest
rate and inflation are linked, and the central bank uses the interest rate to control inflation, it is very
evident that the banking sector would be affected by inflation. When looking at the results of the
energy sector, we have a total of 3 events (2 Overestimation and 1 Underestimation), which have
statistically significant ACAR and AAR on the event date. Two are in the WPI regime and one in CPI.
Here, events 4(Underestimation) and 6(Overestimation) only show a strong presence of significant
abnormal returns before and after the inflation announcement. We don
t see much AAR on the event
date or in the event window, suggesting that the energy sector is not very sensitive to the inflation
announcement. Overestimation events seem to be showing some returns when compared to underestimation and accurate. The rationale could be that an overestimation event overstates inflation.
When inflation is actually below the forecast, this leads to lower energy prices, which would directly
benefit the energy sector as energy prices and inflation are positively related. Hence, underestimation
events give negative returns and overestimation positive for the energy sector. As seen from Figure
2,
there are abnormal returns and, ACAR is very strong for the CPI regime as compared to WPI and IT.
High food inflation has an adverse effect on the FMCG sector, according to economic theory. Four
events are showing the effect of inflation announcement on event date for the FMCG sector. Event 2
Figure 1. AVG CAR of banking
sector.
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(Underestimation), Event 6(Overestimation), Event 8(accurate), and Event 9(Overestimation) are the four
events. The magnitude of the effect of the inflation announcement is the same across all three regimes,
and there seem to be veryfew significant abnormalreturns before and after the announcement date. The
Overestimation events are giving positive returns, while accurate and under-estimation give negative
returns. Figure
3 shows the ACAR for the FMCG sector. The graph seems to be supporting the economic
theory as the ACAR shows extreme negative returns for some events which fall to minus 7% on the event
date. Even the positive ACAR for FMCG is very low compared to all the events.
Housing has 10% weighting in CPI and 0% weighting in WPI, and hence we expect abnormal returns
to be statistically significant during the CPI and IT regimes. As expected, we find no statistically
significant abnormal returns on event date in the WPI regime, whereas, for the CPI regime, we have
two statistically significant abnormal returns on event date for CPI regime and one statistically
significant abnormal return on event date in the IT regime. However, we don
t see much strong
presence of significant abnormal returns before and after the inflation announcement in either of
the regime. Looking at Figure
4, ACAR shows extreme returns compared to other sectors. The event
date returns range from
11% to 6%. WPI measures the prices of goods only, whereas CPI measures
the prices of goods and services. Hence, the CPI index includes services components like health,
education, and transport, which is not present in WPI. However, the result seems to be not in line
with the expectation. We have three statistically significant abnormal returns on the event date, two in
the WPI regime, and one in the CPI regime. The effect also seems to die when India moved from WPI to
CPI and then to IT. Looking at Figure
5, the ACAR for the service sector shows very little abnormal
return during the IT regime. The ACAR is also very less for the service sector compared to all the four
sectors. The reason for low abnormal return in the service sector could be because the service sector
index is made up of 31 stocks. In contrast, the Banking index has 12, the Energy index has 10, the FMCG
index has 15, and the Realty index has ten stocks, respectively.
To sum up, from a market efficiency perspective, Indian markets are not efficient in the short run
to the inflation announcement as there seems to be a considerable amount of abnormal returns
for most of the events. However, the efficiency is varied across the different sectors as some
sectors are more sensitive to inflation announcement and report a higher number of statistically
Figure 2. AVG CAR of energy
sector.
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significant abnormal returns along with high ACAR and AAR. The impact is also a function of the
estimates and inflation switching regime as some sectors are more sensitive in one regime as
compared to others.
5. Conclusion
The study aimed at testing for semi-strong form efficiency in the Indian stock market. We investigate
the effect of inflation announcement on the stock market by looking at the five sectors from 2012 to
Figure 4. AVG CAR of realty
sector.
Figure 3. AVG CAR of FMCG
sector.
Singh & Padmakumari, Cogent Economics & Finance (2020), 8: 1723827
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2018. The estimation period is 90 days and starts from t 100 and ends at t 10 (t being the event
date), while the test period is of 10 days and starts from
t 5 and ends at t þ 5 (t being the event date).
We used the market model to predict future stock returns and further adopted a simple OLS regression
to get the parameters of the regression equation. We then compared the t-statistics to check for
statistically significant abnormal returns. The major finding is that we reject the null hypothesis, AR = 0.
We find strong evidence to reject the hypothesis that Indian markets reflect the information content of
the inflation announcement, and there is no abnormal return. However, the market reaction is
different across sectors and inflation switching regime. The inflation effect seemed to dying as India
moved from WPI to CPI and later when it adopted IT. One explanation is that continued experience of
the IT regime has brought about a greater understanding of the RBI monetary policy decisions. Hence
there are fewer surprises post the IT regime as inflation and interests rate are somewhat predictable.
5.1. Policy and limitation
This study adds to the existing literature by helping in understanding the behavior of the stock
market when there is an inflation announcement in a developing economy like India. Further, it
adds to the existing literature by checking the relationship between inflation announcement
and stock return in the inflation switching environment across sectors, and we find out which
sector is more sensitive to inflationary announcement when compared to others. These results
will help analysts, mutual fund managers, and traders to make an informed decision. However,
the study has some limitations. We have studied only five sectors, and it is difficult to compare
the service sector with other sectors because a large number of stocks have been included in
the services sector index as compared to the realty sector. Hence, the high volatility in the
realty index and low in service sector index could be because of risk getting spread across
many stocks in the service sector. We could also reduce some events and concentrate on a few,
for example studying only Overestimation, Underestimation or accurate estimation of inflation
on a select few sectors for a few events. Lastly, the inflation data generally come around 2:30,
giving very little time to market participants to understand the impact of inflation announcement on the economy as markets close by 3:30. It would be interesting to check the next day
s
open price and see what the overnight effect of the inflation announcement is on the stock
market.
Figure 5. AVG CAR of service
sector.
Singh & Padmakumari, Cogent Economics & Finance (2020), 8: 1723827
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Funding
The authors received no direct funding for this research.
Author details
Gurmeet Singh1
E-mail: [email protected]
Lakshmi Padmakumari2
E-mail: [email protected]
1 Department of Finance, Institute for Financial
Management and Research affiliated to University of
Madras, Alwarpet, Chennai, 600018, India.
2 Department of Finance, Institute for Financial
Management and Research, Alwarpet, Chennai, 600018,
India.
Citation information
Cite this article as: Stock market reaction to inflation
announcement in the Indian stock market: A sectoral
analysis, Gurmeet Singh & Lakshmi Padmakumari
, Cogent
Economics & Finance
(2020), 8: 1723827.
Note
1. All the graphical figures are depicted in Appendix III.
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Appendix A
Appendix B
Figure A1. Depicting the composition of WPI & CPI.
Source: CIEL.
Table B1. Details of events undertaken for the study

Release Date Actual Forecast Over or Under
estimation/
Accurate
Source Inflation
targeting?
14 February 2012 6.60% 6.60% Accurate WPI No
14 September
2012
7.55% 6.95% Underestimation WPI No
15 April 2013 5.96% 6.40% Overestimation WPI No
12 August 2014 7.96% 7.40% Underestimation CPI No
12 September
2014
7.80% 7.80% Accurate CPI No
13 October 2014 6.46% 7.20% Overestimation CPI No
12 May 2016 5.39% 5.00% Underestimation CPI Yes
15 November
2016
4.20% 4.20% Accurate CPI Yes
12 October 2017 3.28% 3.60% Overestimation CPI Yes

Source: https://in.investing.com/economic-calendar/indian-cpi-973.
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